Exploratory analysis of time series data can yield a better understanding of
complex dynamical systems. Granger causality is a practical framework for
analysing interactions in sequential data, applied in a wide range of domains.
In this paper, we propose a novel framework for inferring multivariate Granger
causality under nonlinear dynamics based on an extension of self-explaining
neural networks. This framework is more interpretable than other
neural-network-based techniques for inferring Granger causality, since in
addition to relational inference, it also allows detecting signs of
Granger-causal effects and inspecting their variability over time. In
comprehensive experiments on simulated data, we show that our framework
performs on par with several powerful baseline methods at inferring Granger
causality and that it achieves better performance at inferring interaction
signs. The results suggest that our framework is a viable and more
interpretable alternative to sparse-input neural networks for inferring Granger
causality.

本文提出了一种基于自解释神经网络的非线性动力学下多元格兰杰因果推断的新框架，该框架除了关系推断外，还允许检测格兰杰因果效应的符号并检查其随时间的变化，通过模拟数据等实验表明该框架与其他基线方法相比在推断格兰杰因果性方面具有相当的性能，并在推断交互符号方面取得更好的表现，这表明本框架是推断格兰杰因果性的可行且更易解释的替代方法。